When examining the medical doctrines of previous empires, they reveal the influence of religion, societal attitudes, and the historical context that influenced the scholars that penned them. The advancements during the Islamic Golden age can be seen in the field of medicine, which had the Greco-Roman medical corpus as their foundation and the source of the theory of the four humors and anatomical beliefs. This paper will analyze the effect of cultural, societal, and historical influences on the medical doctrines of Muslim medieval physicians in the Golden Age and the works of the Roman physician Galen, and demonstrate how these effects result in similarities and differences in medical practice and the understanding of disease and anatomy. Due to translation efforts that were supported by religious views on the accumulation of knowledge and the efforts of the Abbasid empire, resultant acceptance of the theory of the four humors and anatomical doctrines is observed in the treatment and perception of disease, which would consist of this paper's focus on surgery, diet therapy and associations with nature. However, with further analysis of the extent of this acceptance and the findings in the Islamic medical doctrines, the differences in experimental methods, religious interpretations, and cultural attitudes shows a deviation from the Galenic tradition, with the second set of the paper's focus being human dissection, cause of disease, and experimentation. The purpose of this research is to demonstrate the impact of religion, societal attitudes, culture and the accepted paradigm on the practice of medicine and the study of anatomy, and what would cause a challenge against the legacy of Galen.
The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.